ai-model-pricing
Server Details
Live pricing data for AI models across vendors: input/output prices per 1k tokens as JSON.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- Repository
- DataLeadsPRO/ai-model-pricing
- GitHub Stars
- 0
- Server Listing
- ai-model-pricing
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusing it with other tools. The tool's purpose is distinct by virtue of being the sole entry point.
With a single tool, there are no naming inconsistencies to evaluate. The name 'ai_models' clearly hints at its domain, even if it does not follow a verb-noun style.
At just one tool, the server feels too thin for a pricing-related API. A single endpoint may be a reasonable MVP, but it lacks the breadth expected from a dedicated pricing service.
Assuming 'ai_models' returns a list of models with pricing details, basic lookups are possible. However, the absence of operations for getting a specific model, calculating costs, or comparing pricing options leaves notable gaps for a pricing-focused server.
Available Tools
1 toolai_modelsDInspect
V1 Ai Models
| Name | Required | Description | Default |
|---|---|---|---|
| extra | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it discloses no behavior whatsoever. It does not mention side effects, permissions, output, or any constraints, leaving the agent completely in the dark.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The text is extremely short, but this is under-specification rather than conciseness. A single vague phrase does not earn credit for brevity when it omits all essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter and no output schema, the description should at least clarify what the parameter does and what the tool returns. It does neither, so the definition is wholly inadequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'extra' has zero schema description and the tool description offers no hint about its meaning, format, or allowed values. An agent cannot know what to pass for this required parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'V1 Ai Models' is a vague label, not a statement of what the tool does. It lacks a verb and resource, and any agent would be unable to infer whether this lists models, generates them, or something else entirely.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool or how it differs from alternatives. There are no siblings listed, but even basic context about the intended use case is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
ai_models
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